@article {tu2015GJIsem,
	title = {Source estimation with surface-related multiples{\textendash}-fast ambiguity-resolved seismic imaging},
	journal = {Geophysical Journal International},
	volume = {205},
	year = {2016},
	note = {(published online in Geophysical Journal International)},
	month = {03},
	pages = {1492-1511},
	abstract = {We address the problem of obtaining a reliable seismic image without prior knowledge of the source wavelet, especially from data that contain strong surface-related multiples. Conventional reverse-time migration requires prior knowledge of the source wavelet, which is either technically or computationally challenging to accurately determine; inaccurate estimates of the source wavelet can result in seriously degraded reverse-time migrated images, and therefore wrong geological interpretations. To solve this problem, we present a "wavelet-free" imaging procedure that simultaneously inverts for the source wavelet and the seismic image, by tightly integrating source estimation into a fast least-squares imaging framework, namely compressive imaging, given a reasonably accurate background velocity model. However, this joint inversion problem is difficult to solve as it is plagued with local minima and the ambiguity with respect to amplitude scalings because of the multiplicative, and therefore nonlinear, appearance of the source wavelet in the otherwise linear formalism. We have found a way to solve this nonlinear joint-inversion problem using a technique called variable projection, and a way to overcome the scaling ambiguity by including surface-related multiples in our imaging procedure following recent developments in surface-related multiple prediction by sparse inversion. As a result, we obtain without prior knowledge of the source wavelet high-resolution seismic images, comparable in quality to images obtained assuming the true source wavelet is known. By leveraging the computationally efficient compressive-imaging methodology, these results are obtained at affordable computational costs compared with conventional processing work flows that include surface-related multiple removal and reverse-time migration.},
	keywords = {computational seismology, free surface, Inverse theory, multiples, seismic imaging, time series analysis, wave propagation},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalJournalInternational/2016/tu2015GJIsem/tu2015GJIsem.pdf},
	url2 = {http://gji.oxfordjournals.org/content/205/3/1492.full.pdf?keytype=ref\&ijkey=zFydRGQpiJzeCS9},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {petrenko2013HPCSsaoc,
	title = {Software acceleration of CARP, an iterative linear solver and preconditioner},
	year = {2013},
	month = {06},
	publisher = {HPCS},
	organization = {HPCS},
	abstract = {We present the results of software optimization of a row-wise preconditioner (Component Averaged Row Projections) for the method of conjugate gradients, which is used to solve the diagonally banded Helmholtz system representing frequency domain, isotropic acoustic seismic wave simulation. We demonstrate that in our application, a preconditioner bound to one processor core and accessing memory contiguously reduces execution time by 7\% for matrices having on the order of 108 non-zeros. For reference we note that our C implementation is over 80 times faster than the corresponding code written for a high-level numerical analysis language.},
	keywords = {frequency-domain, Helmholtz equation, HPCS, Kaczmarz, software, wave propagation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/HPCS/2013/petrenko2013HPCSsaoc/petrenko2013HPCSsaoc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/HPCS/2013/petrenko2013HPCSsaoc/petrenko2013HPCSsaoc_poster.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Felix J. Herrmann}
}
@techreport {petrenko2013SEGsaoc,
	title = {Software acceleration of CARP, an iterative linear solver and preconditioner},
	number = {TR-EOAS-2013-4},
	year = {2013},
	institution = {UBC},
	abstract = {We present the results of software optimization of a row-wise preconditioner (Component Averaged Row Projections) for the method of conjugate gradients, which is used to solve the diagonally banded Helmholtz system representing frequency domain, isotropic acoustic seismic wave simulation. We demonstrate that in our application, a preconditioner bound to one processor core and accessing memory contiguously reduces execution time by 7\% for matrices having on the order of 108 non-zeros. For reference we note that our C implementation is over 80 times faster than the corresponding code written for a high-level numerical analysis language.},
	keywords = {frequency-domain, Helmholtz equation, Kaczmarz, software, wave propagation},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/petrenko2013SEGsaoc/petrenko2013SEGsaoc.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Felix J. Herrmann}
}
